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Abstract

Motivation

Annotated reference corpora play an important role in biomedical information extraction.
A semantic annotation of the natural language texts in these reference corpora using
formal ontologies is challenging due to the inherent ambiguity of natural language.
The provision of formal definitions and axioms for semantic annotations offers the
means for ensuring consistency as well as enables the development of verifiable annotation
guidelines. Consistent semantic annotations facilitate the automatic discovery of
new information through deductive inferences.

Results

We provide a formal characterization of the relations used in the recent GENIA corpus
annotations. For this purpose, we both select existing axiom systems based on the
desired properties of the relations within the domain and develop new axioms for several
relations. To apply this ontology of relations to the semantic annotation of text
corpora, we implement two ontology design patterns. In addition, we provide a software
application to convert annotated GENIA abstracts into OWL ontologies by combining
both the ontology of relations and the design patterns. As a result, the GENIA abstracts
become available as OWL ontologies and are amenable for automated verification, deductive
inferences and other knowledge-based applications.

Availability

Background

The goal of Information Extraction (IE) is to recognize specific pieces of information
in natural language texts and to represent them in a structured form that comprises
meaningful associations of relevant entities. For this reason, IE approaches typically
involve Named Entity Recognition (NER) where mentions of specific types of “real-world” entities, such as people or places, are detected
in text. To facilitate reliable biomedical IE, considerable efforts have been made
with regard to the development of specialized NER methods for key domain entities,
focusing in particular on the recognition of gene and gene product (GGP) mentions
[1-3]. As GGP mentions can further be normalized to identify specific entries in databases
such as UniProt, they provide a connection to entities relevant to biomolecular research
and thus a solid basis for domain IE. However, in contrast to the well-defined meaning
of the basic entities, the semantics of their associations are often only informally
defined.

In biomedical IE, extracted information is frequently represented simply as untyped
pairs of entities representing, for instance, protein-protein or gene-disease associations
[4]. However, even resources identifying protein-protein interactions as entity pairs
diverge considerably in their actual annotations [5], leading to restrictions ranging from usability to interpretability of both the annotations
and IE results. In response to the limitations of such representations, there has
recently been increased interest in richer representations of extracted information
[6] and a number of corpora have been published that annotate associations between entities
by using fine-grained types drawn from ontologies [7,8]. Yet, no definition or axiomatization of these relations has been proposed so far.
Definitions and axioms are necessary to make the meaning of the relations explicit, and to provide the means for developing consistent and
verifiable annotation guidelines allowing for the automatic detection of inconsistent
annotations and enabling the discovery of new information through deductive inferences.
Here, our aim is to define such relations and axioms for fundamental relations such
as part-of connecting GGPs to referents of non-specific domain terms such as promoter region. Annotations to these fundamental relations have been introduced recently [9,10] to the widely used GENIA corpus [11].

Providing formal definitions and axioms for these relations is challenging because
the relation annotations are based on the use of the relations in text, where it is
generally not possible to enforce a common understanding of terms. We extend our preliminary
work [12] and present a formal characterization of the relations used in the GENIA relationship
annotation based on two ontology design patterns. These patterns are not restricted
to an application within the GENIA corpus annotation, but can be applied in a wide
number of domains, in particular in ontology- and knowledge-based applications using
the categories of biological sequences, DNA, RNA or proteins. We implement the developed
formalisms in OWL and provide a conversion software to represent annotated GENIA abstracts
in OWL.

The GENIA corpus

The GENIA corpus consists of 2,000 PubMed abstracts annotated manually by biomedical
domain experts as a resource for the development and evaluation of domain information
extraction (IE) methods. GENIA is one of the most widely used corpora for biomedical
IE and has served as the basis for two community-wide shared tasks on named entity
recognition [1] and event extraction [6]. The annotations of the corpus abstracts include markup that identifies occurrences
of domain terms and named entities, as well as statements of events involving these
terms and entities [8,11,13]. The most recent addition to the corpus annotations covers relations between references
to named entities and other domain terms [9].

Formal ontology

An ontology is the formal specification of a conceptualization of a domain [14]. A conceptualization is a system of classes accounting for a particular view on the
world [15]. Ontologies are used to specify the meaning of terms within a vocabulary. A basic
ontological distinction is made between classes and individuals (or particulars).
A class is an entity that can be predicated of other entities and that can have instances.
The instance-of relation links instances to the class of which they are an instance. Some instances may be classes themselves and have
further instances while an individual is an entity that cannot be further instantiated [16].

For the purpose of formalizing the relations used in the GENIA corpus, we make use
of several biomedical domain ontologies: the Information Artifact Ontology (http://code.google.com/p/information-artifact-ontology/webcite) (IAO), the Sequence Ontology (SO) [17], the Ontology of Biomedical Investigations (OBI) [18], the Gene Ontology (GO) [19] and the GENIA term ontology [11].

Relations in biomedical ontologies can be asserted both between classes and between
individuals [20]. Relations between individuals are used to define the relations between classes.
These definitions may take the form of reusable patterns, and we will create such
patterns for relations between classes in GENIA.

Preliminaries of GENIA corpus annotation

The first question we have to answer before we can formalize relations used in corpus
annotation is what kind of entities are connected through relations in GENIA. Our
first observation is that relations in corpus annotations are usually asserted between
names and other biomedical domain terms, i.e., between strings that are identified
as referring to some kind of entity. While the description of experiments in scientific
publications will commonly refer to collections of individuals and not to classes
[21], the goal of named entity recognition is, among others, the identification of the class to which the characterized collections
belong. Therefore, we assume that the names identified in the GENIA corpus denote
classes.

In some cases, there is ambiguity in determining the referent of a name or domain
term, i.e., certain terms may not refer to identical entities, yet their referents
are regarded as indistinguishable within the context of a task such as the annotation or recognition of named entities.
Regarding certain referents as indistinguishable can improve the automatic extraction
of relations and entities. The indistinguishability assumption also allows the definition
of generic relations that hold between disjoint classes. Through these means, the
effort to create annotation can be reduced, while the applicability of the relations
in different tasks and the feasibility of automatic extraction can be maximized. Within
GENIA annotations [13] and the NER systems based on it, genes and gene products are not distinguished.

Therefore, a basic precursor for our work is an equivalence relation which states
that, within the context of a named entity annotation task, two classes are considered
to be indistinguishable.

Results

Equivalence

Names or terms referring to either a class of genes, DNA, proteins, RNAs and their
splice variants, gene products, arbitrary transcripts or similar are considered to
be equivalent within the context of the GENIA relation annotations. These classes
are called genes/gene products (GGPs). For example, CD19, CD19 protein and CD19 gene may be considered to be equivalent and represent a single GGP.

We define a class GC based on a class C, which is assumed to be a subclass of DNA, and entities derived from C through chains of transcription and translation relations between individuals. The classes Protein, DNA and RNA are those used in the GENIA term ontology.

(1)

Such a formalization has the benefit of connecting the different kinds of GGPs through
formal relations that can be exploited by an automated reasoner.

For example, the name “CD19 protein” refers to a class of proteins, and instances
of this class stand in a translated-from relation to instances of a class of RNA which may be referred to as “CD19 RNA”. Instances
of this class of RNA stand in a transcribed-from relation to instances of a class of DNA which may be referred to as “CD19 gene”.
Thus, according to our definition, all three classes are subclasses of the GGP class
GCD19.

Subclass

The class-subclass relation is used to annotate the relation between terms or names in the GENIA corpus
where one term refers to a more general class than the other term. For example, this
relation holds between the names “CD19 human” (denoting the class CD19 human) and “CD19” (denoting a class that is indistinguishable from the class CD19 (GGP)). We base the definition of the class-subclass relation upon the ontological is-a relation [22]: the classes C and D stand in the is-a relation, if and only if, every instance of C is also an instance of D.

For example, the referent of the name “human CD19 gene” (the class CD19 human gene) stands in the is-a relation to the referent of the name “CD19” (the GGP class CD19 (GGP)), because all instances of CD19 human gene are also instances of CD19 (GGP).

Mereological relations

The largest group of relations in the relationship annotations of the GENIA corpus
refers to mereological relations, i.e., relations between parts and their wholes.
Three kinds of parthood relations are distinguished within GENIA:

• relations between a whole and its components, for example between the classes CD19 promoter and CD19,

• relations between a collection and its members, as between Hox gene family and HOXA1,

• the relation between an entity and the location at which this entity exists, such
as CD19 which is located at CD19 locus.

Substantial work has already been undertaken with regard to mereological relations
and their representation in OWL and biomedical ontologies [20,23,24]. In particular, the relation CC-part-of, as a relation between classes (we generally prefix relations between two classes
with CC-, and relations that hold between two individuals with II-.), must be defined in terms of another relation II-part-of which is a relation between individuals [20,25]. For example, CC-part-of can be defined as C ⊑ ∃II-part-of.D and CC-has-part as C ⊑ ∃II-has-part.D. Although these definitions are valid for many of the parthood relations asserted
between classes in biological ontologies, they are inadequate schemata for parthood
relations which have a GGP class as argument, because the GGP class is “too general”.

However, as a GGP class has several GGP-equivalent subclasses, the CC-has-part and CC-part-of relations may be valid for one of these classes but not for the others. For example,
assuming the definition of CC-has-part above, asserting a CC-has-part relation between the GGP class CD19 (GGP) and CD19 promoter would be incorrect, because the GGP class will also include the CD19 protein class, which has no promoter as part (in virtue of being a class of proteins). Similarly,
although it would be correct to assert that CD19 promoter CC-part-of CD19, it would be incorrect to say that CD19 CC-part-of CD19/CD21/CD81/Leu-13 complex. If the two statements above would hold, we could infer that CD19 promoter is CC-part-of the CD19/CD21/CD81/Leu-13 complex, which is incorrect because protein complexes have no promoters as part.

Consequently, we use the following alternative definition for the GGP-subclass-has-part relation (where the argument GC refers to a GGP class, and X to an arbitrary class):

(2)

In the OWL syntax, a disjunction of axioms is not permitted. Consequently, we have
to reformulate the right side of the definition by using a single subclass axiom (where
⊥ refers to the OWL class owl:Nothing) and derive the equivalent definition:

(3)

Intuitively, this definition states that if the GGP class GC stands in the GGP-subclass-has-part relation to the class X, then either the DNA, RNA or Protein subclass of GC must stand in a CC-has-part relation to X. Using this pattern, we are further able to define the relation GGP-subclass-part-of by replacing II-has-part with II-part-of in definition 3.

II-part-of is a primitive relation and we assert axioms that hold for it. II-part-of is reflexive, transitive and antisymmetric. We define II-proper-part-of:

(4)

It is the II-proper-part-of relation which will provide the basis for the mereological relations within the GENIA,
because identical (or co-extensional) classes are not annotated as standing in a parthood
relation. Parthood relations that are not based upon location are further distinguished
into two kinds in the GENIA relation annotation: a relation between components and
the objects of which they are components, and membership in collections. We assume
that the component-object relation (between individuals) II-oc-part-of is similar to the relation of determinate parthood [23] in that it is reflexive, transitive, antisymmetric and satisfies the strong supplementation
principle [24]. Assuming these axioms for II-oc-part-of provides compatibility with the SO, which also assumes the axioms of extensional
mereology for the entities classified by it [17,26].

The member-component relation, on the other hand, is a relation between entities of
different kinds and is neither reflexive nor antisymmetric [23,27]. The II-member-of relation is a sub-relation of the II-proper-part-of relation and is non-reflexive, asymmetric and non-transitive [27]. II-member-of is not the same relation as the member-of relation in the SO; in the SO, member-of is transitive, while II-member-of is non-transitive. The relation GGP-subclass-member-of holds between a GGP class and a collection, such that for one of the subclasses of
the GGP class, all instances are a member of some instance of the collection. Therefore,
the same pattern as in definition 3 applies for the definition of GGP-member-of. For example, the Lck (GGP) class stands in the GGP-member-of relation to the protein family Src family, because there is a subclass of Lck (GGP), i.e., Lck protein, such that all instances of this subclass stand in an II-member-of relation to some instances of Src family. We do not provide a formal characterization of protein family here, but re-use the class from the GENIA term ontology and represent specific protein
families (such as the Src family) as subclasses of GENIA’s Protein family class. A detailed formal characterization of Protein family within GENIA is subject to future work.

The third parthood relation used in the GENIA corpus annotations is GGP-subclass-region-of, which we define by using the primitive II-region-of relation. In the GENIA relation annotations, GGP-subclass-region-of is used to relate a GGP class to a genomic location. We introduce GGP-subclass-region-of to relate the GGP class to the class of loci. The region is a place where all instances
of one subclass of the GGP class are located. As for the definition of GGP-subclass-has-part, GGP-subclass-part-of and GGP-subclass-member-of, we assume that there is a subclass of the GGP class for which all instances are
located in some instance of the locus, and we use the same pattern as in formula 3.
Next we define the interactions of II-region-of with II-part-of. We want to be able to infer that if the individual x is part of y, and y is located at z, then x is located at z. Furthermore, if the individual x is located at y and y is a part of z, then we infer that x is located at z. We state these conditions using the following axioms in OWL:

(5)

(6)

Objects and their variants

The second major group of GENIA corpus relations connects names of GGP classes to
names of classes of their variants. Again, we formalize the relations that hold between
the classes that are denoted by these names.

The GENIA annotations for GGP classes and their variants use six different relations
to express the following relationships:

We call the basic relation between a GGP and its variant GGP-has-variant. There is a general schema involved in the sub-relations of GGP-has-variant that we exploit in its definition: whenever GGP-has-variant(GC, D), then every instance of D is a variation of some instance of GC. Although it is possible to identify a more specific subclass of GC in some cases, this is not true for all sub-relations of GGP-has-variant. We define the relation GC GGP-has-variant D by using the relation II-has-variant, which is a relation between individuals:

(7)

Again, we provide basic axioms for the II-has-variant relation. Our first observation is that variance is reflexive, i.e., everything (every
molecule) is a variant of itself. Furthermore, variance is symmetric, i.e., if x is a variant of y, then y is a variant of x. Whether II-has-variant is transitive is more difficult to ascertain. While it seems to be the case that,
if x is a variant of y and y a variant of z, then x is a variant of z, this principle may fail if the distance between x and z increases, i.e., more intermediate variants are introduced. Consequently, we do not
assume that II-has-variant is transitive.

To formalize a sub-relation of II-has-variant, e.g., II-has-isoform, we note domain and range of the relation as well as basic axioms. In the definition
of the GGP relation, we must carefully consider whether the relation holds between
all instances of the GGP class, or only one of its subclasses. For example, the definition
of GGP-has-isoform between GC and D is:

(8)

The relations GGP-has-recombinant, GGP-has-precursor and GGP-has-modified-protein follow the same pattern.

II-has-mutant is a relation between an instance of a GGP class and a mutant of this instance. The
relation II-has-mutant is irreflexive and symmetric, and consequently not transitive. The definition of
GC GGP-has-mutant D is as follows:

(9)

II-has-experimental-material relates an instance of a GGP class to experimental material such as an antisense
element. The formal characterization is subject to future work and requires integration
with ontologies of experiments such as the Ontology of Biomedical Investigations (OBI)
[18].

Implementation

To support automatic inferences and verifications, we provide an implementation which
consists of two parts. The first part covers the integration of the basic axioms of
relations between individuals into an OWL ontology. It formalizes GENIA’s relation
ontology and provides the taxonomy of relations as illustrated in figure 1. To be applicable for automated inferences, we had to omit axioms pertaining to reflexivity
or symmetry from the OWL ontology, as those are not permitted for non-primitive properties
[28]. The OWL ontology contains the hierarchy of relations and a single new OWL class,
the class GGP. Furthermore, to provide the definitions of the relations, we also import the OWL
versions of the Sequence Ontology (SO) [17] and the GENIA term ontology [29] so that we can refer to relations such as transcribes-into from the SO, and to classes such as DNA or Protein from the GENIA term ontology. The second part provides a conversion from the relations
between names and terms that refer to classes in OWL. It is a prototypical conversion
tool that translates annotated GENIA abstracts into an OWL file based on the definitions
we provide for GENIA’s relationship annotations. The resulting OWL file is based on
GENIA’s relation ontology. The conversion tool implements the ontology design patterns
we have developed to define relations that take a GGP class as an argument. The conversion
tool and examples of converted abstracts are available on the project website at http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/home/wiki.cgi?page=Relation+annotationwebcite.

Figure 1. Axioms for the relations in the GENIA relation ontology. R stands for reflexivity,
IR for irreflexivity, S for symmetry, T for transitivity, Anti for antisymmetry and
AS for asymmetry.

Discussion

Related work

The BioTop Ontology [30] is derived from the GENIA term ontology and provides definitions and axioms for the
classes in the GENIA ontology. Additionally, this ontology includes several relations.
Some of these relations overlap with those used in the GENIA relation annotation and
in the relation ontology, in particular the mereological relations. Yet, BioTop includes
mostly the generic definitions of mereological relations. Thus, BioTop’s formalization
of mereological relations cannot be used with respect to GGP, as their axioms do not
always hold for GGPs as shown earlier. Furthermore, the BioTop ontology does not include
any of the variance relations. As BioTop provides a rich axiom system for the classes
of the GENIA term ontology, we aim at integrating the BioTop ontology with the relation
ontology and the design patterns we provide in future work.

Another relevant ontology is the Gene Regulation Ontology (GRO) [31], which is an ontology for the domain of gene regulation. It provides axioms and definitions
for the classes DNA, RNA and protein. Furthermore, it establishes relations between
these classes. Therefore, it provides a means for a more detailed specification of
GGP classes. GRO does not cover the relations formalized in this work. Rather, it
could be allow to provide a more fine-grained definition of GGP classes if necessary.

Applications in GENIA

There are several applications of formalized relations within the GENIA corpus:

• development of unambiguous annotator guidelines,

• verification of annotations,

• inference of hidden knowledge and

• abductive reasoning, inductive logic programming, rule learning.

Firstly, the development of clear annotator guidelines can be facilitated to increase
inter-annotator consistency through the provision of less ambiguity. For this purpose,
high expressivity is necessary to specify the meanings of relationship terms or other
terms as precisely as possible. To proceed towards the goal of unambiguous, formal
guidelines for corpus annotation, we used predicate logic for the formalization, and
additionally associated our definitions and axioms with explanations in natural language.

Secondly, the axioms provide a means to verify annotations. Such a verification is made possible because axioms restrict the combinations
of relations and may lead to contradictions which are sometimes automatically detectable.
In particular, the OWL implementation of both the axioms and the ontology design patterns
is amenable to automated reasoning and can be used to detect inconsistencies.

Additionally, it is possible to draw inferences from the asserted knowledge automatically.
These inferences can be used to verify whether or not erroneous annotations have been
asserted by identifying undesired or false inferences. Moreover, automatic inferences
can be used to infer hidden or new knowledge.

The conversion tool we provide converts annotated GENIA abstracts into an OWL ontology.
This conversion is a form of ontology induction or ontology generation. The resulting
ontologies – each covering a domain described within one abstract – can be used for
abductive or inductive logic programming, rule learning or other knowledge-based machine
learning techniques.

Ontology design patterns

To provide definitions for the relations between classes that are used in the GENIA
corpus, we developed two closely related ontology design patterns [32]. They are particularly suited for applications in text mining where the exact referent
of a term cannot always be reliably determined. However, the patterns could be useful
in other domains and applications as well.

The first ontology design pattern is applicable when a class C with the subclasses D1, ..., Dn stands in a relation CC-R to a class E such that every instance of at least one subclass of C stands in a relation II-R to some instance of E. This pattern is useful when one class cannot be entirely disambiguated, and a superclass
is used in a relation statement instead. For example, GGP classes in GENIA are primarily
introduced because it is not always possible – or reasonable – to disambiguate entirely
whether a term refers to DNA, RNA or Protein classes. Instead, the GGP class is used in relation statements, and the GGP class
unifies the classes of DNA, RNA and Protein. In many cases, the relation is only relevant for the instances of one of the subclasses,
e.g. only the Proteins, such that some property or relation applies to every instance of this subclass but
not to all the instances of the other subclasses.

The specialized pattern for a relation GGP-subclass-R is as follows:

(10)

The pattern in formula 10 can be further generalized, as it still uses the classes
DNA, RNA and Protein. In terms of a class C with subclasses D1, ..., Dn whose instances are standing in a relation II-R to some instance of E, the pattern is formulated as follows (where R is the relation between the two classes):

(11)

The second ontology design pattern is derived from the definitions of the has-variant relations. It is applicable when every instance of a GGP class is related by the
relation II-S either to some instance x of a class D, or to some individual which stands in a combination of the relations T1, ..., Tm to x. The general pattern is as follows:

(12)

In general, it is possible to consider either an order defined on the relations T1, ..., Tm or arbitrary permutations. Intuitively, the pattern is used to state that all instances
of one general class (the GGP class in the case of GGP annotations) stand in a relation
II-S to some instance of a class D or to any entity reachable by a chain (or permutation) of the relations T1, ..., Tm from any instance of this class.

Future research

Although the formalization of relationships used in the GENIA annotation is itself
valuable to provide a means for automated inference and verification as well as the
development of annotation guidelines, formalized relations will be much more useful
in combination with a formal characterization of events[8]. Events include more dynamic entities such as the binding of a molecule to a binding site. In conjunction with the formalization of the relations,
more useful inferences would become possible. For example, from the assertion that
a class X binds Y which is a GGP-part-of Z, we would be able to infer that X GGP-binds Y.

We propose ontology design patterns that are not limited to relations between GGPs
but can be applied in many domains. For example, the patterns can be used to formally
distinguish between functions and the processes that realize them when using the functional
abnormality pattern [33,34]. We intend to explore further areas of application beyond the domain of genes and
their products.

Conclusions

We presented and discussed a formal ontology-based characterization of the relations
used for annotating the GENIA corpus. The main challenge was the ambiguity of the
terms upon which the relations are based. These terms refer to one of several ontological
classes, and the definitions of the relationships between two terms had to reflect
that only one of these classes can stand in some relation to another class. To characterize
this phenomenon formally, we introduced the notion of a GGP class, which is an ontological class with subclasses whose names are not distinguishable
within a certain annotation task. In our GENIA use case, the GGP class is a common
superclass for classes of DNA, RNA and proteins, and is intended to unify classes
of genes and their products.

We introduced two ontology design patterns to formally define relations that hold
between a GGP class and another class. The ontology design patterns are especially
useful whenever it is not possible – or not feasible – to determine the exact class
that stands in some relation to another class, and a more general class is chosen
in a relation statement instead. Therefore, they can be generalized to other domains
and applications besides corpus annotation.

We implemented the axioms and definitions as well as the ontology design patterns
in a software application that converts annotated GENIA abstracts into OWL ontologies.
These ontologies can then be used to answer queries, verify annotations or provide
a basis for knowledge-based machine learning techniques. Formalizing the relations
used in the relationship annotations of the GENIA corpus provides a powerful means
to verify the annotations, to reason over them and to establish and communicate unambiguous
and precise annotation guidelines. The ontology of relations, its axioms and our ontology
design patterns are applicable and useful beyond GENIA. They can be integrated in
other ontology- or knowledge-based resources whenever two classes are considered to
be indistinguishable and need to be disambiguated through automated reasoning.

Authors' contributions

RH and AN conceived of the idea to formalize relations in GENIA and developed the
formalism, SP and TO provided the intended meaning of relations and the description
of the annotation process, SP, TO and AO contributed to formalization, RH implemented
the software, DRS supervised the project, RH, AN, SP drafted the initial manuscript,
all authors critically revised the manuscript.

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

The research work in its first unrevised form was presented at the SMBM 2010, Hinxton,
Cambridge, U.K.

This article has been published as part of Journal of Biomedical Semantics Volume 2 Supplement 5, 2011: Proceedings of the Fourth International Symposium on
Semantic Mining in Biomedicine (SMBM). The full contents of the supplement are available
online at http://www.jbiomedsem.com/supplements/2/S5.